A Two-Stage Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks

Wenxin Zhu, Huan Li and Wenhong Wei ()
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Wenxin Zhu: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Huan Li: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Wenhong Wei: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China

Mathematics, 2023, vol. 11, issue 12, 1-13

Abstract: Community detection is a crucial research direction in the analysis of complex networks and has been shown to be an NP-hard problem (a problem that is at least as hard as the hardest problems in nondeterministic polynomial time). Multi-objective evolutionary algorithms (MOEAs) have demonstrated promising performance in community detection. Given that distinct crossover operators are suitable for various stages of algorithm evolution, we propose a two-stage algorithm that uses an individual similarity parameter to divide the algorithm into two stages. We employ appropriate crossover operators for each stage to achieve optimal performance. Additionally, a repair operation is applied to boundary-independent nodes during the second phase of the algorithm, resulting in improved community partitioning results. We assessed the effectiveness of the algorithm by measuring its performance on a synthetic network and four real-world network datasets. Compared to four existing competing methods, our algorithm achieves better accuracy and stability.

Keywords: multi-objective optimization; community detection; complex network; evolutionary algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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